Quantitative Plant Biology

Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.

Growth kinematics and soil mechanics are key to explain how roots overcome the mechanical resistance of soil, yet few studies are linking these two factors. Formulas for cone penetration tests are typically used to infer the friction experienced by roots, but these fail to consider how growth affects the external forces applied on the root. This study formalised how expansive growth in the root apical meristem can reduce soil friction, and applied the framework to analyse the growth strategy of 6 plant species. The results of the analysis revealed trade-offs between reducing frictions, maintaining a desired growth trajectory and elongation rate. A shorter elongation zone can reduce the fraction of the mechanical energy lost to friction, but this is done at the expense of the elongation rate. A sharper tip or increased radius can help roots maintain the elongation rate at no energetic cost, but these strategies come with the cost of growth instability (tortuous roots) and decrease in specific root length respectively. During establishment, root strategies may therefore occupy a 2-dimensional trait space in which the mechanical efficiency of growth is balanced against the explorative-exploitative trade-off. HighlightsGrowth and form of root tips explain how plants overcome mechanical resistance from the soil Trade-offs link the energy lost by friction, growth stability and elongation rate of roots Larger roots allow faster growth independently of these trade-offs New framework formalises plants strategies to acquire soil resources

O_LIIn fluctuating environments, the kinetics of stomatal opening and closing influence the balance between carbon gain and water loss. Smaller guard cells may respond faster to fluctuating environmental conditions because of their greater surface area for osmolyte flux relative to cell volume. A related hypothesis is that operational stomatal conductance (gop) is often well below its theoretical maximum (gmax) because at this stomatal aperture, guard cell volume is poised to change rapidly with small changes in turgor pressure. C_LIO_LIWe analyzed 2,124 estimates of stomatal closure kinetics in response to an abrupt increase in vapor pressure deficit (VPD) among 29 diverse wild tomato populations in the genus Solanum. C_LIO_LILeaves with small guard cells and a lower gop to gmax ratio (fgmax) closed faster, but explained variation in kinetic parameters at different levels of biological organization. Guard cell size had high phylogenetic heritability and varied relatively little within populations, whereas fgmax varied mostly among individuals and between light intensity treatments. C_LIO_LISmaller stomata can be speedier, but only if stomata are held at an aperture where they are responsive to changing turgor pressure. Selection on stomatal speed may influence not only anatomical traits like guard cell size, but also physiological controls on gop. C_LI

The transition to flowering in Arabidopsis thaliana is a complex process governed by many biological and environmental stimuli. Although many of the genes which regulate this process have been identified over the past 30 years, it remains unclear how these networks are integrated. In this study, we used the transcriptional responses of Col-0, Ler-1, and three mutant lines, to build a genome wide regulatory network of Arabidopsis thaliana during the flowering transition. The expression profiles of 22,810 genes across five genotypes were collected from the GEO database Series GSE57 from which we assigned flowering-time genes to different interacting modules by an adapted form of Hierarchical Complete Linkage Clustering (HCLC) after reconstruction of regulatory networks according to the Position Weight Matrix (PWM)-based method. Within these modules, we identified 77 core genes and 31 controller or driver genes. We identify two genes, LHY and, less expectedly, the transcription factor TCP16, to be topographically positioned at the regulatory hubs a nine-gene transcriptional control unit, implying they have the capacity to integrate information from across the flowering time pathways which interpret different environmental or endogenous cues during the vegetative-reproductive transition. Interrogating their behaviour across transcriptional datasets, we show that both LHY and TCP16 show transcriptional oscillations during the flowering transition, with a wavelength that varies depending on environmental conditions. We suggest that the transcriptional responses of LHY and TCP16 allow them to regulate the flow of information through the genetic networks which integrates different floral transition cues, and that genetic modelling approaches can provide new insights into the regulation of well-studied biological processes such as the flowering transition. Author summaryHow plants decide when to flower is a critical stage for completing their life cycles. It is also of key agricultural importance, as crops need to flower at the right time of year to allow efficient pollination and harvesting. Many genes are known to affect flowering time control in plants. Here, we use computational approaches to estimate how different genes interact in flowering time control in Arabidopsis, a small plant in the mustard family which is widely used for molecular studies. We use large-scale studies of how gene expression changes in different plant lines which have disrupted or adjusted flowering time to group the many genes involved in flowering into different interacting pathway, which we visualise as sets of coloured nodes controlling one another in a network. We show that two genes may have new rols in integrating information from different pathways, and discuss how their behaviour might help them to function as intregrators of biological information - including the daily oscaillations in their expression.

The assimilation of carbon dioxide by plants can be predicted by the Farquhar, von Caemmerer and Berry model of photosynthesis. This largely mechanistic model is central to understanding how plants influence Earths climate. However, it represents the use of light by photosynthesis using an empirical formulation. Johnson and Berry proposed an alternative mechanistic formulation based on the functioning of the cytochrome b6f complex that includes key steps in light harvesting and electron transport. We compared both formulations using photosynthetic light response measurements from 146 C3 species spanning arctic to tropical biomes and implemented them in the terrestrial biosphere model ELM-FATES to simulate global photosynthesis. The Johnson and Berry formulation better fitted the measured response of leaf-level photosynthesis to light, and predicted lower photosynthetic rates at intermediate light levels, which decreased global estimations of terrestrial photosynthesis by 8%. Our findings support adopting the Johnson and Berry formulation to improve model representation of global carbon cycle modeling.

Mechanical stimulation of the root triggers signal transduction involving Reactive Oxygen Species (ROS) and calcium, but their relationships are unclear. This study aims to clarify the temporal and spatial interrelations between calcium and ROS following a localized lateral compression of the root. We combined a microfluidic valve rootchip to apply controlled compression, with fluorescent probes and wide-field or confocal microscopy to monitor H2O2 and calcium dynamics in root tissues simultaneously. Pharmacological inhibitors were used to investigate the causal links between H2O2 and calcium responses. In response to compression, we observed transient H2O2 accumulation, with characteristics similar to the calcium response observed previously in the same microfluidic system. H2O2 and calcium response occurred in 3 kinetic phases: a fast calcium increase relying on mechanosensitive channels and external calcium entry, followed by a long-lasting H2O2 accumulation and a slow calcium increase depending on NADPH oxidase activity. H2O2 accumulated in all root tissues while calcium increases were confined to the root center. These results suggest that two mechanotransduction mechanisms are involved in root response to compression. One mechanism relies on plasma membrane mechanosensitive channels, triggering a fast calcium increase. Another independent mechanism, relying on FERONIA, induces H2O2 accumulation, which drives the slower secondary calcium increase.

C4 photosynthesis evolved from the ancestral C3 pathway through coordinated leaf anatomical and metabolic reorganization that concentrates CO2 to reduce photorespiration. Quantitative understanding of these structure-function relationships remains limited. Here we used anatomy-aware metabolic modeling of a mesophyll-bundle sheath cell system to analyze the interdependence between leaf anatomy and photosynthetic metabolism on the C3-C4 spectrum. Our model faithfully recapitulates the transitory steps from C3 to C4 photosynthesis, reveals a crucial role for plasmodesmata in enabling the C3 to C4 transition, and points at potential pre-C2 metabolic states that provide benefits under conditions that favor elevated photorespiration. Incorporating bundle cell suberisation with our model predicts reduction of PSII activity and dominance of the NADP-ME C4 subtype in leaves with suberized bundle sheath cells and proposes a role for oxygen evolution at PSII as a potential driver for this mechanism. Varying bundle sheath leakage and photorespiratory conditions along the C3-C4 spectrum identify conditions under which C3-C4 intermediate photosynthesis provides energetic benefits and underlines the notion of intermediate photosynthesis as a stable evolutionary state. Overall, our study sheds new light on the quantitative relationship between leaf anatomy and metabolism and its interaction with the environment and suggests targets for climate-adaptation in C3 plants.

The photosynthesis-transpiration-stomatal conductance (An-E-gs) model framework is widely used for estimating photosynthesis, transpiration, and stomatal conductance in plants. The model equations are solved by numerical iteration, and the converged model values are deemed the solution. However, there has been no general guarantee that the iterative procedure converges to a solution or that the procedure leads to convergence. Building on the recent proof of the existence of a unique set of solutions, we herewith propose a numerical algorithm that is guaranteed to converge to the solution for the An-E-gs model framework. We first analytically prove that the proposed algorithm necessarily converges to a solution. We then demonstrate the convergence across contrasting combinations of leaf temperature, relative humidity, light, atmospheric CO2, and wind speed. We further demonstrate rapid convergence with the algorithm: no more than ca. 10 iterations for approximately 10-3 mol CO2 m-2 s-1 precision in net photosynthesis and no more than ca. 20 iterations for 10-7 mol CO2 m-2 s-1 precision. By guaranteeing convergence to the solution, this algorithm eliminates concerns about nonconvergence in leaf gas-exchange calculations and is expected to serve as a robust foundation for a range of studies from leaf-level gas exchange to global-scale carbon and water cycle dynamics.

Multi-environment trials are central to cultivar evaluation because they reveal how candidate cultivars perform across locations, years, management conditions, and stress environments. The resulting yield matrix is a rich source of data on genotype-by-environment interaction, and a wide literature on estimation, decomposition, visualisation, and prediction of yield potential and stability has flourished. However the ultimate question of which cultivar to recommend on the basis of such a matrix is often left implicit. The question is far from trivial, and in this paper we formulate cultivar recommendation as an axiomatic ranking problem. This framework is rich enough to encompass the existing literature on stability indices, as well as any other deterministic ranking procedure. We show that many commonly used stability-based procedures can violate minimal criteria of efficiency or consistency. The result of such violations is that a cultivar with uniformly high yield could be ranked below a cultivar with uniformly low yield, or the relative ranks of two cultivars could depend on whether or not a third cultivar is present in the matrix. Our results prove that under a small number of such criteria the space of admissible rules collapses to the family of power means and their limiting cases. If we further wish to allow multiplication normalisation of yield, we are left with the geometric mean as the unique solution.

Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf and stomatal traits. However, traditional trait-based and phylogenetic approaches often fail to fully explain biochemical mechanisms underlying ecological strategies, particularly for leaf and stomatal traits. Plant metabolomes integrate genetic, physiological, and environmental information and therefore represent a promising intermediate phenotype for investigating links between biochemical diversity, functional traits, and evolutionary patterns. We analysed metabolomic profiles from 74 plant species with various growth forms and ecological types. Using machine learning approaches, we explored whether metabolic variation could predict plant functional divisions, growth forms and phenological types, but also physiological traits related to drought resistance. Metabolomic data contained structured information associated with variation in plant functional traits, ecological strategies, and phylogenetic relationships. Machine learning models identified with high accuracy distinct metabolic signatures linked to differences among plant functional divisions, growth forms, phenology, and trait values. Our study demonstrates that predictive metabolomics provides a powerful and integrative framework to investigate plant ecological and evolutionary strategies. By linking biochemical diversity with plant phylogeny, and ecophysiological traits across multiple species, this approach offers new opportunities to explore the mechanistic basis of plant evolution.

Plant photosynthesis operates under naturally fluctuating light, yet its dynamic responses across timescales remain incompletely understood. Here, we apply sinusoidal light modulation as a controlled periodic input and analyze the response in the frequency domain, enabling quantitative system identification of photosynthetic dynamics. Using a minimal biochemical model of photosynthetic electron transport and regulation, we show that the system exhibits distinct dynamic regimes separated by a characteristic timescale of approximately 10 s. In the high-frequency domain, the response is governed by constitutive processes and reflects steady-state properties such as the plastoquinone redox state. In the low-frequency domain, regulatory feedback dominates, particularly non-photochemical quenching (NPQ), which modulates both the amplitude and phase of the response. For small-amplitude perturbations, the system behaves linearly and can be characterized using transfer functions and Bode plots. We show that key physiological parameters, including relaxation times and regulatory gains, can be extracted directly from frequency-response features such as phase maxima and gain transitions. In the nonlinear regime, large-amplitude oscillations generate higher-harmonic structure and alter time-averaged photosynthetic performance relative to constant illumination. We further introduce the concept of regulation fingerprints, defined as ratios of transfer functions between regulated and unregulated systems. These fingerprints reveal distinct spectral signatures of fast (PsbS-mediated) and slow (zeaxanthin-dependent) NPQ processes, enabling their quantitative separation. Together, these results establish frequency-domain analysis as a framework for probing and identifying the dynamic regulation of photosynthesis under fluctuating light, with direct applicability to non-invasive measurements in laboratory and field conditions.

O_LIPlants continuously adjust photosynthesis to balance growth and photoprotection under changing environmental conditions. Environmental fluctuations frequently impose a mismatch between energy production and CO2 assimilation. How photochemical reactions are regulated to maintain performance under these conditions remains a central question in plant biology. C_LIO_LIWe previously developed transplastomic tobacco (Fd1-OE plants) overexpressing ferredoxin (Fd) displaying enhanced photoprotection and growth penalties with a variegated leaf phenotype under greenhouse conditions. Here, we investigate how these plants respond to different growth irradiances using physiological, ultrastructural, and photosynthetic analyses, including PAM, gas exchange, and P700 absorbance measurements, and dynamic-light assays. C_LIO_LIFd1-OE plants progressively recovered growth, leaf phenotype and photosynthetic performance as growth irradiance increased, reaching near WT performance at 1400 mol m-{superscript 2} s-{superscript 1}. This enhanced adaptation to "high-light" was associated with a larger fraction of open PSII reaction centers and enhanced NPQ. Dynamic-light analyses further revealed faster plastoquinone (PQ) turnover, a more oxidized PQ pool and enhanced electron withdrawal downstream of PSI. C_LIO_LIOur results indicate that Fd overexpression redefines the balance between photochemistry and photoprotection. This adjustment shifts adaptation toward higher irradiance and enhances photosynthetic performance under changing light environments. Electron partitioning downstream of PSI emerges as a promising target to improve photosynthetic resilience. C_LI One sentence summaryOverexpression of Fd1 in tobacco plants adjusts photosynthesis/photoprotection trade off to enhance high-light adaptation

The article introduces a new Forest Stress Index (ISF) based on a plant hydraulic modelling approach rather than classical climatic drought indices. Unlike other index like scPDSI or SPEI, ISF is grounded in xylem embolism dynamics simulated with the mechanistic SurEau model. The goal is to better link climatic anomalies to tree physiological functioning and mortality risk. ISF is defined using a locally adapted ideotype characterized by an optimal P50 value under a reference hydraulic functioning threshold. Simulations are performed across Europe and France using multiple climate datasets. The index is robust to model parameterization choices and assumptions about plant functional traits. Results show strong spatial and temporal consistency and significant correlations with SPEI and scPDSI. However, ISF more strongly highlights extreme drought years and exhibits a more skewed distribution. Future projections under SSP5-8.5 indicate a widespread increase in hydraulic stress with strong regional contrasts. Overall, ISF provides a mechanistic and complementary drought indicator more directly linked to forest mortality processes.

Understanding plant growth dynamics requires imaging across day-and-night cycles to quantify growth, movement and development in the aerial plant body and to capture the rhythmic nature of these processes. This requires imaging in light during the day and in darkness at night without perturbing plant physiology. Nighttime imaging has typically depended on infrared (IR) illumination, producing monochrome datasets that require specialised hardware and separate analysis pipelines when combined with daytime RGB imaging. Here, we evaluated very low-intensity green (dimG) illumination from standard LEDs as a practical alternative for colour-consistent nighttime imaging and assessed its physiological impact in Arabidopsis thaliana and Lactuca sativa (lettuce). We show that high resolution colour images can be obtained under dimG using low- cost cameras, with sufficient consistency between full-spectrum and dimG images to allow direct comparison and unified image analysis. We show that very low-fluence green light (<0.5 mol m-2 s-1) does not sustain circadian oscillations of gene activity under continuous exposure and does not perturb rhythms when applied during the dark phase of diel cycles. DimG imaging enabled accurate detection of diel leaf movement profiles in Arabidopsis circadian mutants, revealing genotype-specific phase differences under varying photoperiods. In lettuce, dimG pulses and continuous dimG enabled accurate quantification of diel leaf movement without affecting growth, stomatal opening, electron transport rate or chlorophyll content. Motion profiles under continuous dimG mirrored those under darkness. Our findings establish dim green illumination as a cost-effective solution for night-time imaging, simplifying phenotyping workflows with minimal impact on physiology.

Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cue to the later stages of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of injury-associated cold response and downstream salicylic acid (SA) signaling. The CBF-SA module promotes lignin deposition and wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.

1Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900-1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R2) of 0.771 {+/-} 0.066 and a root mean squared error (RMSE) of 0.743 {+/-} 0.098 mg g-1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.

Changes in gene expression over time can provide valuable insights into developmental processes and responses to the environment. Differences in expression may be indicative of potential differences in regulation. Comparing transcript dynamics may help identify correspondences between developmental stages within and between species, differences in the timing of key events during development, and transcriptional response to treatments or perturbations. A straightforward comparison between the dynamics is, however, hindered by measurements that were taken at different time points and over different timescales. To address this, we developed a statistical approach that seeks the optimal alignment between two time series as a function of a temporal shift and stretch. We validated our approach using simulated data and applied it to several transcriptome datasets, including comparisons between different plant species. Our development facilitates knowledge transfer from model systems to less studied species, the identification of modules of co-regulated genes, and the discovery of condition-specific, temporally differentially-expressed genes. The method is provided freely available as an R package.

In developing tissues, cells differentiate into distinct cell types and form complex spatial patterns. How distinct patterning systems interact during tissue growth to shape tissue composition and spatial organization remains poorly understood. Here, we investigate this question in the abaxial leaf epidermis of Arabidopsis thaliana, in which the same pool of progenitor cells gives rise to stomata, pavement cells, and giant cells. Using a quantitative approach combining Euclidean and network-based spatial analysis, we show that stomatal number and density are robust to reduced endoreduplication, whereas forced endoreduplication actively competes with the stomatal lineage to reduce stomatal number. Furthermore, we show that the stomatal spatial pattern is also shaped by the broader tissue context such as cell growth, cell division, and giant cell patterning, with distinct consequences for stomatal spatial distribution and cellular arrangement. Our results highlight that the interplay between patterning systems must be considered to understand how tissue organization is established.

Understanding stress-induced metabolic reprogramming in crop plants can inform breeding strategies and support the development of stress-resilient varieties. Genome-scale metabolic modelling has shown promise in elucidating network-level responses to changing environments, yet as an optimality-based approach it relies on the definition of an objective function, which is far from trivial for non-optimal conditions. To address this uncertainty, we used a time-resolved, data-informed metabolic model of rice (Oryza sativa L.) cold stress response as a test case, and explored two complementary approaches. We used sampling of the solution space combined with machine learning to identify reactions and pathways best characterizing the stress-induced metabolic shift, and used this information to perform Pareto analysis, placing growth and a stress-related objective in competition. This trade-off analysis identified key branch points in carbohydrate, amino acid, phenylpropanoid, nucleotide, and fatty acid biosynthesis, where resource reallocation towards stress-protection comes at the expense of growth. It further revealed differential flux modes across subcellular compartments and shifts in reducing equivalent provision as distinguishing features of the stress response. Together, these results provide a mechanistic understanding of the metabolic trade-offs and branch points governing cold stress response, and identify potential targets to optimize the cold response-growth trade-off in rice.

Phenomic prediction (PP) is a breeding value prediction method using near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, there is still little understanding in the genetics community of what pre-processing does and why it increases performances. Consequently, the choice of pre-processing is done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose a PCA-based pre-processing method where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative and orthogonal features of spectra instead of many correlated, uninformative wavelengths. We tested this new pre-processing method on five data sets representing four plant species (maize, rice, sorghum and grapevine). Results show that it performs as good, or better than the best classical chemometric pre-processing methods in almost all cases. Combining PCA-based and classical chemometric pre-processing methods maximizes predictive ability. Moreover, this pre-processing method opens up possibilities of better understanding and selecting parts of the spectral information that are relevant for the prediction of breeding values. Indeed, components representing together about 1% of spectral variability were found to be responsible for most of PP predictive ability. Plain language summaryCultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimation of breeding values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput genetic value estimation method that is increasingly being used. It often uses near infrared spectroscopy measurements as predictors of genetic values that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a new pre-processing approach that performs as good as or better than the best chemometric pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core IdeasO_LIWorking on principal components of spectra instead of wavelengths increases predictive ability of phenomic prediction and performs as good as or better than classical chemometrics pre-processing C_LIO_LIWorking on principal components of spectra requires less optimization of parameters than chemometrics pre-processing C_LIO_LIAbout 1% of spectral variance is responsible for most of the predictive power of phenomic prediction C_LIO_LIWorking on principal components of spectra pre-processed with classical chemometrics pre-processing can increase predictive ability even more C_LIO_LIPCA-based methods are valuable to optimize predictive ability of phenomic prediction and could be used more widely in the quantitative genetics field C_LI